
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


from warnings import filterwarnings
filterwarnings('ignore')


data_path = r'C:\Users\kesha\Desktop\Zomato ML\zomato.csv'
from pandas import read_csv


df= read_csv(data_path)

# Results
print(f'Dataset shape: {df.shape}')
df.head()

Dataset shape: (51717, 17)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51717 entries, 0 to 51716
Data columns (total 17 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   url                          51717 non-null  object
 1   address                      51717 non-null  object
 2   name                         51717 non-null  object
 3   online_order                 51717 non-null  object
 4   book_table                   51717 non-null  object
 5   rate                         43942 non-null  object
 6   votes                        51717 non-null  int64 
 7   phone                        50509 non-null  object
 8   location                     51696 non-null  object
 9   rest_type                    51490 non-null  object
 10  dish_liked                   23639 non-null  object
 11  cuisines                     51672 non-null  object
 12  approx_cost(for two people)  51371 non-null  object
 13  reviews_list                 51717 non-null  object
 14  menu_item                    51717 non-null  object
 15  listed_in(type)              51717 non-null  object
 16  listed_in(city)              51717 non-null  object
dtypes: int64(1), object(16)
memory usage: 6.7+ MB


df.isnull().sum()

url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64


#getting nan feature
feature_na=[feature for feature in df.columns if df[feature].isnull().sum()>0]
feature_na

['rate',
 'phone',
 'location',
 'rest_type',
 'dish_liked',
 'cuisines',
 'approx_cost(for two people)']


#% of missing values
import numpy as np
for feature in feature_na:
    print('{} has {} % missing values'.format(feature,np.round(df[feature].isnull().sum()/len(df)*100,4)))

rate has 15.0337 % missing values
phone has 2.3358 % missing values
location has 0.0406 % missing values
rest_type has 0.4389 % missing values
dish_liked has 54.2916 % missing values
cuisines has 0.087 % missing values
approx_cost(for two people) has 0.669 % missing values


df['approx_cost(for two people)'].dtype

dtype('O')


df[df['approx_cost(for two people)'].isnull()]


df['approx_cost(for two people)'].unique()

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',
       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',
       '1,600', '230', '130', '50', '190', '1,700', nan, '1,400', '180',
       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',
       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',
       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',
       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',
       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)


# right now it has some NAN Values so it will be of float data-type,dats why very first I have to convert it into string then
#I have to remove this comma
df['approx_cost(for two people)'] = df['approx_cost(for two people)'].astype(str).apply(lambda x: x.replace(',', ''))


df['approx_cost(for two people)']=df['approx_cost(for two people)'].astype(float)


df['approx_cost(for two people)'].dtype

dtype('float64')


df['rate'].unique()

array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',
       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',
       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',
       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',
       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',
       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',
       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',
       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',
       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


df['rate'][0].split('/')[0]

'4.1'


def split(x):
    return x.split('/')[0]


df['rate'].dtype

dtype('O')


df['rate'].isnull().sum()

7775


# right now it has some NAN Values so it will be of float data-type,dats why very first I have to convert it into string then
# I have to split it & access 
df['rate']=df['rate'].astype(str).apply(split)
# df['rate'] = df['rate'].astype(str).apply(lambda x: x.split('/')[0])


df['rate'].replace('NEW',0,inplace=True)
df['rate'].replace('-',0,inplace=True)
df['rate']=df['rate'].astype(str).astype(float)
df['rate'].dtype

dtype('float64')


import matplotlib.pyplot as plt
plt.figure(figsize=(20,12))
df['rest_type'].value_counts().nlargest(20).plot.bar(color='red')

# to provide styling to text on x-axis
plt.gcf().autofmt_xdate()


df.columns

Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')


df['rest_type'].value_counts()

Quick Bites                   19132
Casual Dining                 10330
Cafe                           3732
Delivery                       2604
Dessert Parlor                 2263
                              ...  
Dessert Parlor, Food Court        2
Bakery, Food Court                2
Food Court, Beverage Shop         2
Sweet Shop, Dessert Parlor        1
Quick Bites, Kiosk                1
Name: rest_type, Length: 93, dtype: int64


def mark(x):
    if x in ('Quick Bites', 'Casual Dining'):
        return 'Quick Bites + Casual Dining'
    else:
        return 'other'


df['Top_types']=df['rest_type'].apply(mark)
df.head()


import plotly.express as px
values=df['Top_types'].value_counts()
labels=df['Top_types'].value_counts().index


fig = px.pie(df, values=values, names=labels,title='Restaurants Pie chart')
fig.show()


df.head()


df.columns

Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)', 'Top_types'],
      dtype='object')


df.dtypes

url                             object
address                         object
name                            object
online_order                    object
book_table                      object
rate                           float64
votes                            int64
phone                           object
location                        object
rest_type                       object
dish_liked                      object
cuisines                        object
approx_cost(for two people)    float64
reviews_list                    object
menu_item                       object
listed_in(type)                 object
listed_in(city)                 object
Top_types                       object
dtype: object


rest=df.groupby('name').agg({'votes': 'sum','url': 'count','approx_cost(for two people)': 'mean','rate': 'mean'}).reset_index()
rest


rest.columns = ['name', 'total_votes', 'total_unities', 'avg_approx_cost', 'mean_rating']
rest.head()


rest['votes_per_unity'] = rest['total_votes'] / rest['total_unities']
rest.head()


popular=rest.sort_values(by='total_unities', ascending=False)
popular


popular['name'].nunique()

8792


popular.shape

(8792, 6)


import seaborn as sns
# Creating a figure for restaurants overview analysis
fig, (ax1,ax2,ax3) = plt.subplots(3,1, figsize=(20,30))

# Plot Pack 01 - Most popular restaurants (votes)

# Annotations
ax1.text(0.50, 0.30, int(popular['total_votes'].mean()), fontsize=45, ha='center')
ax1.text(0.50, 0.12, 'is the average of votes', fontsize=12, ha='center')
ax1.text(0.50, 0.00, 'received by restaurants', fontsize=12, ha='center')
ax1.axis('off')

sns.barplot(x='total_votes', y='name', data=popular.sort_values(by='total_votes', ascending=False)[0:5],ax=ax2, palette='plasma')
ax2.set_title('Top 5 Most Voted Restaurants', size=12)

sns.barplot(x='total_votes', y='name', data=popular.sort_values(by='total_votes', ascending=False).query('total_votes > 0').tail(),ax=ax3, palette='plasma_r')
ax3.set_title('Top 5 Less Voted Restaurants\n(with at least 1 vote)', size=12)

Text(0.5, 1.0, 'Top 5 Less Voted Restaurants\n(with at least 1 vote)')


popular.columns

Index(['name', 'total_votes', 'total_unities', 'avg_approx_cost',
       'mean_rating', 'votes_per_unity'],
      dtype='object')


popular.head()


fig, (ax1,ax2,ax3) = plt.subplots(3,1, figsize=(20,30))
# Annotations
import numpy as np
ax1.text(0.50, 0.30, np.round(popular['avg_approx_cost'].mean(), 2), fontsize=45, ha='center')
ax1.text(0.50, 0.12, 'is mean approx cost', fontsize=12, ha='center')
ax1.text(0.50, 0.00, 'for Bengaluru restaurants', fontsize=12, ha='center')
ax1.axis('off')

sns.barplot(x='avg_approx_cost', y='name', data=popular.sort_values(by='avg_approx_cost', ascending=False)[0:5],ax=ax2, palette='plasma')
ax2.set_title('Top 5 Most Expensives Restaurants', size=12)

sns.barplot(x='avg_approx_cost', y='name', data=popular.sort_values(by='avg_approx_cost', ascending=False).query('avg_approx_cost > 0').tail(),ax=ax3, palette='plasma_r')
ax3.set_title('Top 5 Less Expensive Restaurants', size=12)

Text(0.5, 1.0, 'Top 5 Less Expensive Restaurants')


import plotly.graph_objs as go
from plotly.offline import iplot
x=df['book_table'].value_counts()
labels=['not book','book']


trace=go.Pie(labels=labels, values=x,
               hoverinfo='label+percent', textinfo='percent', 
               textfont=dict(size=25),
              pull=[0, 0, 0,0.2, 0]
               )
iplot([trace])


import plotly.express as px
x=df['online_order'].value_counts()
labels=['accepted','not accepted']


fig = px.pie(df, values=x, names=labels,title='Pie chart')
fig.show()


def return_budget(location,restaurant):
    budget=df[(df['approx_cost(for two people)']<=400) & (df['location']==location) & 
                     (df['rate']>4) & (df['rest_type']==restaurant)]
    return(budget['name'].unique())


return_budget('BTM',"Quick Bites")

array(['Swadista Aahar', 'Litti Twist', 'The Shawarma Shop', 'Gorbandh',
       'Yum In My Tum', 'Chaatimes', "Muthashy's", 'Swad Punjab Da',
       "Domino's Pizza", 'Roti Wala', 'Andhra Kitchen'], dtype=object)


locations=pd.DataFrame({"Name":df['location'].unique()})


# For Bangalore
locations['new_Name']='Bangalore '+locations['Name']


locations.head()


!pip install geopy

Collecting geopy
  Downloading geopy-2.2.0-py3-none-any.whl (118 kB)
Collecting geographiclib<2,>=1.49
  Downloading geographiclib-1.52-py3-none-any.whl (38 kB)
Installing collected packages: geographiclib, geopy
Successfully installed geographiclib-1.52 geopy-2.2.0

WARNING: You are using pip version 21.1.2; however, version 21.2.1 is available.
You should consider upgrading via the 'c:\users\kesha\anaconda3\python.exe -m pip install --upgrade pip' command.


from geopy.geocoders import Nominatim


lat=[]
lon=[]
geolocator=Nominatim(user_agent="app")
for location in locations['Name']:
    location = geolocator.geocode(location)
    if location is None:
        lat.append(np.nan)
        lon.append(np.nan)
    else:    
        lat.append(location.latitude)
        lon.append(location.longitude)


locations['latitude']=lat
locations['longitude']=lon


locations.to_csv('zomato_locations.csv',index=False)


Rest_locations=pd.DataFrame(df['location'].value_counts().reset_index())


Rest_locations.columns=['Name','count']
Rest_locations.head()


Restaurant_locations=Rest_locations.merge(locations,on='Name',how="left").dropna()
Restaurant_locations.head()


def generateBaseMap(default_location=[12.97, 77.59], default_zoom_start=12):
    base_map = folium.Map(location=default_location, zoom_start=default_zoom_start)
    return base_map


import folium
from folium.plugins import HeatMap
basemap=generateBaseMap()


data=df[df['rest_type']=='Quick Bites']


data['dish_liked']

3                                              Masala Dosa
31       Burgers, Lassi, Chicken Grill, Naan, Momos, Ch...
34       Rolls, Veggie Wrap, Chocolate Fantasy, Rice Bo...
36                                                     NaN
39                                                     NaN
                               ...                        
51641                                                  NaN
51642                                                  NaN
51643                                                  NaN
51644                                                  NaN
51645                                              Burgers
Name: dish_liked, Length: 19132, dtype: object


df['reviews_list'][0]
data=df['reviews_list'][0].lower()
data

'[(\'rated 4.0\', \'rated\\n  a beautiful place to dine in.the interiors take you back to the mughal era. the lightings are just perfect.we went there on the occasion of christmas and so they had only limited items available. but the taste and service was not compromised at all.the only complaint is that the breads could have been better.would surely like to come here again.\'), (\'rated 4.0\', \'rated\\n  i was here for dinner with my family on a weekday. the restaurant was completely empty. ambience is good with some good old hindi music. seating arrangement are good too. we ordered masala papad, panner and baby corn starters, lemon and corrionder soup, butter roti, olive and chilli paratha. food was fresh and good, service is good too. good for family hangout.\\ncheers\'), (\'rated 2.0\', \'rated\\n  its a restaurant near to banashankari bda. me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet. on inquiring they said this place is mostly visited by vegetarians. anyways we ordered ala carte items which took ages to come. food was ok ok. definitely not visiting anymore.\'), (\'rated 4.0\', \'rated\\n  we went here on a weekend and one of us had the buffet while two of us took ala carte. firstly the ambience and service of this place is great! the buffet had a lot of items and the good was good. we had a pumpkin halwa intm the dessert which was amazing. must try! the kulchas are great here. cheers!\'), (\'rated 5.0\', \'rated\\n  the best thing about the place is itã\x83\\x83ã\x82\\x83ã\x83\\x82ã\x82\\x82ã\x83\\x83ã\x82\\x82ã\x83\\x82ã\x82\\x92s ambiance. second best thing was yummy ? food. we try buffet and buffet food was not disappointed us.\\ntest ?. ?? ?? ?? ?? ??\\nquality ?. ??????????.\\nservice: staff was very professional and friendly.\\n\\noverall experience was excellent.\\n\\nsubirmajumder85.wixsite.com\'), (\'rated 5.0\', \'rated\\n  great food and pleasant ambience. expensive but coll place to chill and relax......\\n\\nservice is really very very good and friendly staff...\\n\\nfood : 5/5\\nservice : 5/5\\nambience :5/5\\noverall :5/5\'), (\'rated 4.0\', \'rated\\n  good ambience with tasty food.\\ncheese chilli paratha with bhutta palak methi curry is a good combo.\\nlemon chicken in the starters is a must try item.\\negg fried rice was also quite tasty.\\nin the mocktails, recommend "alice in junoon". do not miss it.\'), (\'rated 4.0\', \'rated\\n  you canã\x83\\x83ã\x82\\x83ã\x83\\x82ã\x82\\x82ã\x83\\x83ã\x82\\x82ã\x83\\x82ã\x82\\x92t go wrong with jalsa. never been a fan of their buffet and thus always order alacarteã\x83\\x83ã\x82\\x83ã\x83\\x82ã\x82\\x82ã\x83\\x83ã\x82\\x82ã\x83\\x82ã\x82\\x92. service at times can be on the slower side but food is worth the wait.\'), (\'rated 5.0\', \'rated\\n  overdelighted by the service and food provided at this place. a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic. i would totally recommend to visit this place once.\'), (\'rated 4.0\', \'rated\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\n\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\'), (\'rated 4.0\', \'rated\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\n\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\'), (\'rated 4.0\', \'rated\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\n\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\')]'


import re
data2=re.sub('[^a-zA-Z]', ' ',data)
data2

'   rated        rated n  a beautiful place to dine in the interiors take you back to the mughal era  the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available  but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again       rated        rated n  i was here for dinner with my family on a weekday  the restaurant was completely empty  ambience is good with some good old hindi music  seating arrangement are good too  we ordered masala papad  panner and baby corn starters  lemon and corrionder soup  butter roti  olive and chilli paratha  food was fresh and good  service is good too  good for family hangout  ncheers      rated        rated n  its a restaurant near to banashankari bda  me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet  on inquiring they said this place is mostly visited by vegetarians  anyways we ordered ala carte items which took ages to come  food was ok ok  definitely not visiting anymore       rated        rated n  we went here on a weekend and one of us had the buffet while two of us took ala carte  firstly the ambience and service of this place is great  the buffet had a lot of items and the good was good  we had a pumpkin halwa intm the dessert which was amazing  must try  the kulchas are great here  cheers       rated        rated n  the best thing about the place is it   x     x     x     x     x     x     x     x  s ambiance  second best thing was yummy   food  we try buffet and buffet food was not disappointed us  ntest                   nquality                nservice  staff was very professional and friendly  n noverall experience was excellent  n nsubirmajumder   wixsite com      rated        rated n  great food and pleasant ambience  expensive but coll place to chill and relax       n nservice is really very very good and friendly staff    n nfood       nservice       nambience      noverall           rated        rated n  good ambience with tasty food  ncheese chilli paratha with bhutta palak methi curry is a good combo  nlemon chicken in the starters is a must try item  negg fried rice was also quite tasty  nin the mocktails  recommend  alice in junoon   do not miss it       rated        rated n  you can   x     x     x     x     x     x     x     x  t go wrong with jalsa  never been a fan of their buffet and thus always order alacarte   x     x     x     x     x     x     x     x    service at times can be on the slower side but food is worth the wait       rated        rated n  overdelighted by the service and food provided at this place  a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic  i would totally recommend to visit this place once       rated        rated n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome      rated        rated n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome      rated        rated n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome   '


data3=re.sub('rated', ' ',data2)
data3

'              n  a beautiful place to dine in the interiors take you back to the mughal era  the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available  but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again                  n  i was here for dinner with my family on a weekday  the restaurant was completely empty  ambience is good with some good old hindi music  seating arrangement are good too  we ordered masala papad  panner and baby corn starters  lemon and corrionder soup  butter roti  olive and chilli paratha  food was fresh and good  service is good too  good for family hangout  ncheers                 n  its a restaurant near to banashankari bda  me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet  on inquiring they said this place is mostly visited by vegetarians  anyways we ordered ala carte items which took ages to come  food was ok ok  definitely not visiting anymore                  n  we went here on a weekend and one of us had the buffet while two of us took ala carte  firstly the ambience and service of this place is great  the buffet had a lot of items and the good was good  we had a pumpkin halwa intm the dessert which was amazing  must try  the kulchas are great here  cheers                  n  the best thing about the place is it   x     x     x     x     x     x     x     x  s ambiance  second best thing was yummy   food  we try buffet and buffet food was not disappointed us  ntest                   nquality                nservice  staff was very professional and friendly  n noverall experience was excellent  n nsubirmajumder   wixsite com                 n  great food and pleasant ambience  expensive but coll place to chill and relax       n nservice is really very very good and friendly staff    n nfood       nservice       nambience      noverall                      n  good ambience with tasty food  ncheese chilli paratha with bhutta palak methi curry is a good combo  nlemon chicken in the starters is a must try item  negg fried rice was also quite tasty  nin the mocktails  recommend  alice in junoon   do not miss it                  n  you can   x     x     x     x     x     x     x     x  t go wrong with jalsa  never been a fan of their buffet and thus always order alacarte   x     x     x     x     x     x     x     x    service at times can be on the slower side but food is worth the wait                  n  overdelighted by the service and food provided at this place  a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic  i would totally recommend to visit this place once                  n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome                 n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome                 n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome   '


data4=re.sub('x',' ',data3)
data4

'              n  a beautiful place to dine in the interiors take you back to the mughal era  the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available  but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again                  n  i was here for dinner with my family on a weekday  the restaurant was completely empty  ambience is good with some good old hindi music  seating arrangement are good too  we ordered masala papad  panner and baby corn starters  lemon and corrionder soup  butter roti  olive and chilli paratha  food was fresh and good  service is good too  good for family hangout  ncheers                 n  its a restaurant near to banashankari bda  me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet  on inquiring they said this place is mostly visited by vegetarians  anyways we ordered ala carte items which took ages to come  food was ok ok  definitely not visiting anymore                  n  we went here on a weekend and one of us had the buffet while two of us took ala carte  firstly the ambience and service of this place is great  the buffet had a lot of items and the good was good  we had a pumpkin halwa intm the dessert which was amazing  must try  the kulchas are great here  cheers                  n  the best thing about the place is it                                                s ambiance  second best thing was yummy   food  we try buffet and buffet food was not disappointed us  ntest                   nquality                nservice  staff was very professional and friendly  n noverall e perience was e cellent  n nsubirmajumder   wi site com                 n  great food and pleasant ambience  e pensive but coll place to chill and rela        n nservice is really very very good and friendly staff    n nfood       nservice       nambience      noverall                      n  good ambience with tasty food  ncheese chilli paratha with bhutta palak methi curry is a good combo  nlemon chicken in the starters is a must try item  negg fried rice was also quite tasty  nin the mocktails  recommend  alice in junoon   do not miss it                  n  you can                                                t go wrong with jalsa  never been a fan of their buffet and thus always order alacarte                                                  service at times can be on the slower side but food is worth the wait                  n  overdelighted by the service and food provided at this place  a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic  i would totally recommend to visit this place once                  n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome                 n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome                 n  the place is nice and comfortable  food wise all jalea outlets maintain a good standard  the soya chaap was a standout dish  clearly one of trademark dish as per me and a must try  n nthe only concern is the parking  it very congested and limited to just  cars  the basement parking is very steep and makes it cumbersome   '


re.sub(' +',' ',data4)

' n a beautiful place to dine in the interiors take you back to the mughal era the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again n i was here for dinner with my family on a weekday the restaurant was completely empty ambience is good with some good old hindi music seating arrangement are good too we ordered masala papad panner and baby corn starters lemon and corrionder soup butter roti olive and chilli paratha food was fresh and good service is good too good for family hangout ncheers n its a restaurant near to banashankari bda me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet on inquiring they said this place is mostly visited by vegetarians anyways we ordered ala carte items which took ages to come food was ok ok definitely not visiting anymore n we went here on a weekend and one of us had the buffet while two of us took ala carte firstly the ambience and service of this place is great the buffet had a lot of items and the good was good we had a pumpkin halwa intm the dessert which was amazing must try the kulchas are great here cheers n the best thing about the place is it s ambiance second best thing was yummy food we try buffet and buffet food was not disappointed us ntest nquality nservice staff was very professional and friendly n noverall e perience was e cellent n nsubirmajumder wi site com n great food and pleasant ambience e pensive but coll place to chill and rela n nservice is really very very good and friendly staff n nfood nservice nambience noverall n good ambience with tasty food ncheese chilli paratha with bhutta palak methi curry is a good combo nlemon chicken in the starters is a must try item negg fried rice was also quite tasty nin the mocktails recommend alice in junoon do not miss it n you can t go wrong with jalsa never been a fan of their buffet and thus always order alacarte service at times can be on the slower side but food is worth the wait n overdelighted by the service and food provided at this place a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic i would totally recommend to visit this place once n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome '


dataset=df[df['rest_type']=='Quick Bites']
type(dataset['reviews_list'][3])

str


total_review=' '
for review in dataset['reviews_list']:
    review=review.lower()
    review=re.sub('[^a-zA-Z]', ' ',review)
    review=re.sub('rated', ' ',review)
    review=re.sub('x',' ',review)
    review=re.sub(' +',' ',review)
    total_review=total_review + str(review)


df.columns

Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)', 'Top_types'],
      dtype='object')


df['rate'].unique()

array([4.1, 3.8, 3.7, 3.6, 4.6, 4. , 4.2, 3.9, 3.1, 3. , 3.2, 3.3, 2.8,
       4.4, 4.3, 0. , 2.9, 3.5, nan, 2.6, 3.4, 4.5, 2.5, 2.7, 4.7, 2.4,
       2.2, 2.3, 4.8, 4.9, 2.1, 2. , 1.8])


# Splitting restaurants
def assign(x):
    if x>0:
        return 1
    else:
        return 0
df['rated']=df['rate'].apply(assign)


df['rated'].unique()

array([1, 0], dtype=int64)


new_restaurants = df[df['rated'] == 0]
train_val_restaurants = df.query('rated == 1')


train_val_restaurants.head()


train_val_restaurants['rate'].unique()

array([4.1, 3.8, 3.7, 3.6, 4.6, 4. , 4.2, 3.9, 3.1, 3. , 3.2, 3.3, 2.8,
       4.4, 4.3, 2.9, 3.5, 2.6, 3.4, 4.5, 2.5, 2.7, 4.7, 2.4, 2.2, 2.3,
       4.8, 4.9, 2.1, 2. , 1.8])


# Defining a custom threshold for splitting restaurants into good and bad
threshold = 3.75
train_val_restaurants['target'] = train_val_restaurants['rate'].apply(lambda x: 1 if x >= threshold else 0)


train_val_restaurants.head()


import matplotlib.pyplot as plt
x=train_val_restaurants['target'].value_counts()
labels=x.index
print(x)
plt.pie(x,explode=[0.0,0.1],autopct='%1.1f%%')

0    21421
1    20244
Name: target, dtype: int64

([<matplotlib.patches.Wedge at 0x1bd200f1250>,
  <matplotlib.patches.Wedge at 0x1bd200f1850>],
 [Text(-0.04879500100763322, 1.0989172161162393, ''),
  Text(0.05323091019014493, -1.1988187812177156, '')],
 [Text(-0.026615455095072665, 0.5994093906088577, '51.4%'),
  Text(0.031051364277584537, -0.699310955710334, '48.6%')])


train_val_restaurants.columns

Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)', 'Top_types', 'rated', 'target'],
      dtype='object')


train_val_restaurants.head()


# train_val_restaurants['total_cuisines'] = train_val_restaurants['cuisines'].astype(str).apply(lambda x: len(x.split(',')))

def count(x):
    return len(x.split(','))


#as it have some NAN value that why very first I have to convert into str  &  then apply a function
train_val_restaurants['total_cuisines']=train_val_restaurants['cuisines'].astype(str).apply(count)
train_val_restaurants['multiple_types']=train_val_restaurants['rest_type'].astype(str).apply(count)


train_val_restaurants.columns

Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)', 'Top_types', 'rated', 'target',
       'total_cuisines', 'multiple_types'],
      dtype='object')


imp_features=['online_order','book_table','location','rest_type','multiple_types','total_cuisines','listed_in(type)', 'listed_in(city)','approx_cost(for two people)','target']


data = train_val_restaurants[imp_features]


data.isnull().sum()

online_order                     0
book_table                       0
location                         0
rest_type                      149
multiple_types                   0
total_cuisines                   0
listed_in(type)                  0
listed_in(city)                  0
approx_cost(for two people)    247
target                           0
dtype: int64


data.dropna(how='any',inplace=True)


data.isnull().sum()

online_order                   0
book_table                     0
location                       0
rest_type                      0
multiple_types                 0
total_cuisines                 0
listed_in(type)                0
listed_in(city)                0
approx_cost(for two people)    0
target                         0
dtype: int64


# Splitting features by data type
cat_features= [col for col in data.columns if data[col].dtype == 'O']
num_features= [col for col in data.columns if data[col].dtype != 'O']


cat_features

['online_order',
 'book_table',
 'location',
 'rest_type',
 'listed_in(type)',
 'listed_in(city)']


for feature in cat_features:
    print('{} has total {} unique features'.format(feature, data[feature].nunique()))

online_order has total 2 unique features
book_table has total 2 unique features
location has total 92 unique features
rest_type has total 87 unique features
listed_in(type) has total 7 unique features
listed_in(city) has total 30 unique features


data.shape

(41271, 10)


cols=['location','rest_type','listed_in(city)']
for col in cols:
    print('Total feature in {} are {}'.format(col,data[col].nunique()))
    print(data[col].value_counts()/(len(data))*100)
    print('\n')

Total feature in location are 92
BTM                      9.398851
Koramangala 5th Block    5.565651
HSR                      4.829057
Indiranagar              4.361416
JP Nagar                 4.143345
                           ...   
Yelahanka                0.009692
West Bangalore           0.007269
Rajarajeshwari Nagar     0.004846
Nagarbhavi               0.002423
Peenya                   0.002423
Name: location, Length: 92, dtype: float64


Total feature in rest_type are 87
Quick Bites              33.643478
Casual Dining            23.299654
Cafe                      8.163117
Dessert Parlor            4.482566
Delivery                  4.048848
                           ...    
Cafe, Food Court          0.004846
Dessert Parlor, Kiosk     0.004846
Bakery, Food Court        0.004846
Bakery, Beverage Shop     0.002423
Quick Bites, Kiosk        0.002423
Name: rest_type, Length: 87, dtype: float64


Total feature in listed_in(city) are 30
BTM                      6.265901
Koramangala 7th Block    5.720724
Koramangala 4th Block    5.461462
Koramangala 5th Block    5.451770
Koramangala 6th Block    5.139202
Jayanagar                4.647331
JP Nagar                 3.959197
Indiranagar              3.726588
Church Street            3.678127
MG Road                  3.668435
Brigade Road             3.593322
Lavelle Road             3.515786
Residency Road           3.258947
HSR                      3.222602
Marathahalli             3.137797
Bannerghatta Road        2.926995
Whitefield               2.914880
Old Airport Road         2.842189
Brookefield              2.762230
Basavanagudi             2.597466
Kammanahalli             2.473892
Sarjapur Road            2.466623
Kalyan Nagar             2.408471
Bellandur                2.316397
Frazer Town              2.309127
Malleshwaram             2.292166
Rajajinagar              2.112864
Electronic City          1.986867
Banashankari             1.763950
New BEL Road             1.378692
Name: listed_in(city), dtype: float64


percent=data['location'].value_counts()/len(data)*100
values=percent.values
len(values[values>0.4])

46


# set Threshold value 0.4 
values=data['location'].value_counts()/len(data)*100
values

BTM                      9.398851
Koramangala 5th Block    5.565651
HSR                      4.829057
Indiranagar              4.361416
JP Nagar                 4.143345
                           ...   
Yelahanka                0.009692
West Bangalore           0.007269
Rajarajeshwari Nagar     0.004846
Nagarbhavi               0.002423
Peenya                   0.002423
Name: location, Length: 92, dtype: float64


threshold=0.4
imp=values[values>threshold]
imp

BTM                      9.398851
Koramangala 5th Block    5.565651
HSR                      4.829057
Indiranagar              4.361416
JP Nagar                 4.143345
Jayanagar                3.959197
Whitefield               3.808970
Marathahalli             3.416443
Bannerghatta Road        2.970609
Koramangala 7th Block    2.556274
Koramangala 6th Block    2.553851
Brigade Road             2.549005
Bellandur                2.415740
Sarjapur Road            2.069250
Koramangala 1st Block    2.064404
Ulsoor                   2.057135
Koramangala 4th Block    2.037750
Electronic City          2.020789
MG Road                  1.921446
Banashankari             1.802719
Kalyan Nagar             1.681568
Malleshwaram             1.553149
Residency Road           1.463497
Richmond Road            1.463497
Basavanagudi             1.441690
Frazer Town              1.371423
Church Street            1.322963
Brookefield              1.315694
New BEL Road             1.226042
Kammanahalli             1.194543
Lavelle Road             1.165467
Cunningham Road          1.150929
Banaswadi                1.133968
Rajajinagar              1.117007
Domlur                   0.983742
Shanti Nagar             0.891667
Old Airport Road         0.855322
St. Marks Road           0.831092
Shivajinagar             0.671173
Commercial Street        0.654212
Ejipura                  0.620290
Jeevan Bhima Nagar       0.598483
Vasanth Nagar            0.593637
Koramangala 8th Block    0.513678
Koramangala 3rd Block    0.462795
Wilson Garden            0.453103
Name: location, dtype: float64


data['location']=np.where(data['location'].isin(imp.index),data['location'],'other')


data['location'].nunique()

47


values2=data['rest_type'].value_counts()/len(data)*100
values2

Quick Bites              33.643478
Casual Dining            23.299654
Cafe                      8.163117
Dessert Parlor            4.482566
Delivery                  4.048848
                           ...    
Cafe, Food Court          0.004846
Dessert Parlor, Kiosk     0.004846
Bakery, Food Court        0.004846
Bakery, Beverage Shop     0.002423
Quick Bites, Kiosk        0.002423
Name: rest_type, Length: 87, dtype: float64


data['rest_type'].head(20)

0           Casual Dining
1           Casual Dining
2     Cafe, Casual Dining
3             Quick Bites
4           Casual Dining
5           Casual Dining
6           Casual Dining
7     Casual Dining, Cafe
8                    Cafe
9                    Cafe
10                   Cafe
11                   Cafe
12                   Cafe
13                   Cafe
14    Cafe, Casual Dining
15                   Cafe
16                   Cafe
17                   Cafe
18                   Cafe
19                   Cafe
Name: rest_type, dtype: object


len(values2[values2>0.3])

29


threshold=1.5
imp2=values2[values2>1.5]
imp2

Quick Bites           33.643478
Casual Dining         23.299654
Cafe                   8.163117
Dessert Parlor         4.482566
Delivery               4.048848
Takeaway, Delivery     3.099028
Casual Dining, Bar     2.645926
Bakery                 1.705798
Beverage Shop          1.555572
Bar                    1.550726
Name: rest_type, dtype: float64


imp2.index

Index(['Quick Bites', 'Casual Dining', 'Cafe', 'Dessert Parlor', 'Delivery',
       'Takeaway, Delivery', 'Casual Dining, Bar', 'Bakery', 'Beverage Shop',
       'Bar'],
      dtype='object')


data['rest_type'].isin(imp2.index)

0         True
1         True
2        False
3         True
4         True
         ...  
51709     True
51711     True
51712     True
51715     True
51716    False
Name: rest_type, Length: 41271, dtype: bool


data['rest_type']=np.where(data['rest_type'].isin(imp2.index),data['rest_type'],'other')


data['rest_type']

0             Casual Dining
1             Casual Dining
2                     other
3               Quick Bites
4             Casual Dining
                ...        
51709    Casual Dining, Bar
51711    Casual Dining, Bar
51712                   Bar
51715                   Bar
51716                 other
Name: rest_type, Length: 41271, dtype: object


for feature in cat_features:
    print('{} has total {} unique features'.format(feature, data[feature].nunique()))

online_order has total 2 unique features
book_table has total 2 unique features
location has total 47 unique features
rest_type has total 11 unique features
listed_in(type) has total 7 unique features
listed_in(city) has total 30 unique features


cat_features

['online_order',
 'book_table',
 'location',
 'rest_type',
 'listed_in(type)',
 'listed_in(city)']


import pandas as pd
data_cat = data[cat_features]
for col in cat_features:
    col_encoded = pd.get_dummies(data_cat[col],prefix=col,drop_first=True)
    data_cat=pd.concat([data_cat,col_encoded],axis=1)
    data_cat.drop(col, axis=1, inplace=True)


data_cat.shape

(41271, 93)


data_cat.head(10)


data_cat.shape

(41271, 93)


data.head()


data_final=pd.concat([data.loc[:,['multiple_types','total_cuisines','approx_cost(for two people)','target']],data_cat],axis=1)


data_final.shape

(41271, 97)


# Splitting the data
X = data_final.drop('target', axis=1)
y = data_final['target'].values


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, random_state=42)


X_train.shape

(33016, 96)


# Import the random forest model.
from sklearn.ensemble import RandomForestClassifier


# Initialize the model with some parameters.
model = RandomForestClassifier(n_estimators=100, min_samples_leaf=10, random_state=1)


model.fit(X_train, y_train)

RandomForestClassifier(min_samples_leaf=10, random_state=1)


# Make predictions.
predictions = model.predict(X_test)

# Compute the error.
from sklearn.metrics import confusion_matrix
confusion_matrix(predictions, y_test)

array([[3556, 1283],
       [ 696, 2720]], dtype=int64)


from sklearn.metrics import accuracy_score
accuracy_score(predictions,y_test)

0.7602665051483949


from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier


models = []
models.append(('LogisticRegression', LogisticRegression()))
models.append(('Naive Bayes',GaussianNB()))
models.append(('RandomForest', RandomForestClassifier()))
models.append(('Decision Tree', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier(n_neighbors = 5)))


for name,model in models:
    print(name)
    print(models)

LogisticRegression
[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]
Naive Bayes
[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]
RandomForest
[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]
Decision Tree
[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]
KNN
[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]


for name, model in models:
    print(name)
    model.fit(X_train, y_train)
    
    # Make predictions.
    predictions = model.predict(X_test)

    # Compute the error.
    from sklearn.metrics import confusion_matrix
    print(confusion_matrix(predictions, y_test))

    from sklearn.metrics import accuracy_score
    print(accuracy_score(predictions,y_test))
    print('\n')

LogisticRegression
[[3462 1523]
 [ 790 2480]]
0.7198061780738946


Naive Bayes
[[3040 1460]
 [1212 2543]]
0.6763173834039976


RandomForest
[[3502  936]
 [ 750 3067]]
0.7957601453664446


Decision Tree
[[3634  811]
 [ 618 3192]]
0.826892792247123


KNN
[[3623 1006]
 [ 629 2997]]
0.8019382192610539

	name	votes	url	approx_cost(for two people)	rate
0	#FeelTheROLL	14	2	200.0	3.400000
1	#L-81 Cafe	432	9	400.0	3.900000
2	#Vibes Restro	0	3	700.0	NaN
3	#refuel	111	3	400.0	3.700000
4	'Brahmins' Thatte Idli	0	1	100.0	NaN
...	...	...	...	...	...
8787	late100	0	5	200.0	NaN
8788	nu.tree	1443	8	400.0	4.314286
8789	re:cess - Hilton Bangalore Embassy GolfLinks	438	3	1200.0	4.100000
8790	repEAT Hub	0	2	200.0	NaN
8791	sCoolMeal	0	5	300.0	NaN

	name	total_votes	total_unities	avg_approx_cost	mean_rating
0	#FeelTheROLL	14	2	200.0	3.4
1	#L-81 Cafe	432	9	400.0	3.9
2	#Vibes Restro	0	3	700.0	NaN
3	#refuel	111	3	400.0	3.7
4	'Brahmins' Thatte Idli	0	1	100.0	NaN

	name	total_votes	total_unities	avg_approx_cost	mean_rating	votes_per_unity
0	#FeelTheROLL	14	2	200.0	3.4	7.0
1	#L-81 Cafe	432	9	400.0	3.9	48.0
2	#Vibes Restro	0	3	700.0	NaN	0.0
3	#refuel	111	3	400.0	3.7	37.0
4	'Brahmins' Thatte Idli	0	1	100.0	NaN	0.0

	name	total_votes	total_unities	avg_approx_cost	mean_rating	votes_per_unity
1320	Cafe Coffee Day	3089	96	844.791667	3.147191	32.177083
5549	Onesta	347520	85	600.000000	4.410588	4088.470588
3788	Just Bake	2898	73	400.000000	3.355882	39.698630
2446	Empire Restaurant	229808	71	685.211268	3.916901	3236.732394
2577	Five Star Chicken	3134	70	257.857143	3.425000	44.771429
...	...	...	...	...	...	...
1900	Cool Break	11	1	150.000000	3.400000	11.000000
8076	The Shake Factory Originals	8	1	200.000000	3.300000	8.000000
5216	Nethravathi Military Hotel	0	1	200.000000	NaN	0.000000
7426	Swadisht North Indian Restaurant	23	1	200.000000	3.200000	23.000000
5375	Night Punjabi Folk	0	1	200.000000	NaN	0.000000

	name	total_votes	total_unities	avg_approx_cost	mean_rating	votes_per_unity
1320	Cafe Coffee Day	3089	96	844.791667	3.147191	32.177083
5549	Onesta	347520	85	600.000000	4.410588	4088.470588
3788	Just Bake	2898	73	400.000000	3.355882	39.698630
2446	Empire Restaurant	229808	71	685.211268	3.916901	3236.732394
2577	Five Star Chicken	3134	70	257.857143	3.425000	44.771429

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
0	https://www.zomato.com/bangalore/jalsa-banasha...	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	Yes	Yes	4.1/5	775	080 42297555\r\n+91 9743772233	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	[('Rated 4.0', 'RATED\n A beautiful place to ...	[]	Buffet	Banashankari
1	https://www.zomato.com/bangalore/spice-elephan...	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	Yes	No	4.1/5	787	080 41714161	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	[('Rated 4.0', 'RATED\n Had been here for din...	[]	Buffet	Banashankari
2	https://www.zomato.com/SanchurroBangalore?cont...	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	Yes	No	3.8/5	918	+91 9663487993	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	[('Rated 3.0', "RATED\n Ambience is not that ...	[]	Buffet	Banashankari
3	https://www.zomato.com/bangalore/addhuri-udupi...	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	No	No	3.7/5	88	+91 9620009302	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	[('Rated 4.0', "RATED\n Great food and proper...	[]	Buffet	Banashankari
4	https://www.zomato.com/bangalore/grand-village...	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	No	No	3.8/5	166	+91 8026612447\r\n+91 9901210005	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	[('Rated 4.0', 'RATED\n Very good restaurant ...	[]	Buffet	Banashankari

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
1662	https://www.zomato.com/bangalore/papa-khan-ras...	6, Abbiah Layout KC Halli Main Road, Bommanaha...	Papa Khan Rasoi	No	No	NaN	0	NaN	NaN	NaN	NaN	NaN	NaN	[]	[]	Delivery	Bannerghatta Road
1768	https://www.zomato.com/bangalore/innate-jp-nag...	45, 14th Main, 7th phase, Puttehahalli Palya, ...	Innate	No	No	NaN	0	NaN	JP Nagar	Delivery	NaN	Beverages	NaN	[]	[]	Delivery	Bannerghatta Road
1795	https://www.zomato.com/bangalore/super-chefs-b...	8/3, Opposite Suryadhoya Convention hall, Bann...	Super Chef's (New Royal treat)	No	No	NaN	0	NaN	Bannerghatta Road	Quick Bites	NaN	North Indian, Chinese, South Indian	NaN	[('Rated 1.0', 'RATED\n Many items were missi...	[]	Delivery	Bannerghatta Road
3069	https://www.zomato.com/bangalore/super-chefs-b...	8/3, Opposite Suryadhoya Convention hall, Bann...	Super Chef's (New Royal treat)	No	No	-	0	NaN	Bannerghatta Road	Quick Bites	NaN	North Indian, Chinese, South Indian	NaN	[('Rated 1.0', 'RATED\n Many items were missi...	[]	Delivery	Basavanagudi
3774	https://www.zomato.com/bangalore/the-hub-ibis-...	Ibis Bengaluru, Opposite RMZ Ecospace Business...	The Hub - Ibis Bengaluru	No	No	3.9/5	237	NaN	Marathahalli	Bar	Dal Makhani, Cocktails, Lunch Buffet, Breakfas...	Finger Food	NaN	[('Rated 4.0', 'RATED\n The Hub is a small ba...	[]	Buffet	Bellandur
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
50940	https://www.zomato.com/bangalore/cinnabon-whit...	Ground Floor, The Forum Neighborhood Mall, Pre...	Cinnabon	No	No	4.0 /5	80	NaN	Whitefield	Quick Bites	Rolls, Cinnamon Roll, Cinnamon Bun	Beverages, American, Bakery	NaN	[('Rated 4.0', "RATED\n Who else is a Cinnabo...	[]	Desserts	Whitefield
51233	https://www.zomato.com/bangalore/mayas-kitchen...	43/9, Borewell Rd, Palm Meadows, Nallurhalli, ...	Mayas kitchen	No	No	NaN	0	NaN	Whitefield	Casual Dining	NaN	South Indian, Chinese, Chettinad	NaN	[('Rated 4.0', "RATED\n Food was pretty good,...	[]	Dine-out	Whitefield
51247	https://www.zomato.com/bangalore/mast-kalandar...	D64, 33, 1st Floor, Sundari Armadale, Next To ...	Mast Kalandar	No	No	2.6 /5	76	NaN	Whitefield	Casual Dining	Gulab Jamun, Lassi, Dal Makhani, Buttermilk, T...	North Indian	NaN	[('Rated 1.0', 'RATED\n Terrible food in this...	[]	Dine-out	Whitefield
51271	https://www.zomato.com/bangalore/karavalli-foo...	N254-107, 1st Main Road, Near ITPL Gate 3, KIA...	Karavalli Food Court	No	No	3.2 /5	4	NaN	Whitefield	Casual Dining	NaN	South Indian	NaN	[('Rated 1.0', 'RATED\n i was a regular custo...	[]	Dine-out	Whitefield
51641	https://www.zomato.com/bangalore/andhra-spices...	188, ITPL Main Road, Brookefields, Bangalore, ...	Andhra Spices	No	No	3.2 /5	4	NaN	Brookefield	Quick Bites	NaN	Andhra, Chinese, South Indian	NaN	[('Rated 2.0', "RATED\n Food is not upto the ...	[]	Dine-out	Whitefield

	Name	new_Name
0	Banashankari	Bangalore Banashankari
1	Basavanagudi	Bangalore Basavanagudi
2	Mysore Road	Bangalore Mysore Road
3	Jayanagar	Bangalore Jayanagar
4	Kumaraswamy Layout	Bangalore Kumaraswamy Layout

	Name	count
0	BTM	5124
1	HSR	2523
2	Koramangala 5th Block	2504
3	JP Nagar	2235
4	Whitefield	2144

	Name	count	new_Name	latitude	longitude
0	BTM	5124	Bangalore BTM	45.954851	-112.496595
1	HSR	2523	Bangalore HSR	18.147500	41.538889
2	Koramangala 5th Block	2504	Bangalore Koramangala 5th Block	12.934377	77.628415
3	JP Nagar	2235	Bangalore JP Nagar	12.265594	76.646540
4	Whitefield	2144	Bangalore Whitefield	44.373058	-71.611858

	online_order_Yes	book_table_Yes	location_Banashankari	location_Basavanagudi	...
0	1	1	1	0	...
1	1	0	1	0	...
2	1	0	1	0	...
3	0	0	1	0	...
4	0	0	0	1	...
5	1	0	0	1	...
6	0	0	0	0	...
7	1	1	1	0	...
8	1	0	1	0	...
9	1	0	1	0	...

	online_order_Yes	book_table_Yes	location_Banashankari	location_Basavanagudi	...
0	1	1	1	0	...
1	1	0	1	0	...
2	1	0	1	0	...
3	0	0	1	0	...
4	0	0	0	1	...
5	1	0	0	1	...
6	0	0	0	0	...
7	1	1	1	0	...
8	1	0	1	0	...
9	1	0	1	0	...

	online_order_Yes	book_table_Yes	location_Banashankari	location_Basavanagudi	...
0	1	1	1	0	...
1	1	0	1	0	...
2	1	0	1	0	...
3	0	0	1	0	...
4	0	0	0	1	...
5	1	0	0	1	...
6	0	0	0	0	...
7	1	1	1	0	...
8	1	0	1	0	...
9	1	0	1	0	...